This is a birthday gift I built from scratch for my girlfriend 🙂
The hardware is made of a broken Interactive R2D2 toy and a bunch of cheap parts purchased online. Inside, the little guy is powered by a Raspberry Pi running Rasbian.
voice control (in English and Chinese, using PocketSphinx)
face recognition (using OpenCV)
ultrasonic distance detection
audio message record and replay
sound play and TTS
Facebook’s Caffe2 AI tools travel to iPhone, Android, and Raspberry Pi
Your mobile phone may soon have the ability to understand things in photos without the need of accessing the cloud
New intelligence can be included in mobile devices like the apple iphone, Android gadgets, and low-power personal computers similar to Raspberry Pi with Facebook’s new open-source Caffe2 deep-learning framework.
Caffe2 enables you to program artificial intelligence features into tablets and smart phones, allowing them to acknowledge graphics, video clip, textual content, and speech and be more situationally aware.
You should take note that Caffe2 isn’t an Artificial intelligence program, but a tool allowing for AI to be programmed into mobile phones. It takes just some lines of code to write learning models, which could then be incorporated into apps.
The introduction of Caffe2 is really important. It indicates users will be able to get image recognition, natural language processing, and computer vision straight on their phone. That work is often offloaded to remote servers in the cloud, with mobile phones then connecting to it.
Mobile gadgets are receiving more artificial intelligence abilities. More smartphones are being bundled up with Amazon’s Alexa and Google Assistant, while Apple’s Siri has been a staple in the iPhone for a long time. Samsung’s Galaxy S8 mobile phones are set to get the Bixby voice assistant, that ought to make operating the handsets much easier.
Caffe2 can function within the power constraints of mobile gadgets. It works with mobile hardware to quicken AI applications and create neural networks.
Caffe2 takes advantage of the computing power of new mobile hardware to hasten deep-learning tasks. As an illustration, in mobile phones, Caffe2 will take advantage of the computing power of Adreno GPUs and Hexagon DSPs on Qualcomm’s Snapdragon cell SoC.